The field of the invention relates generally to computer-implemented programs and, more particularly, to a computer-implemented system with adaptive cognitive learning features.
Most known supply chains include a system of organizations, people, and technology that initiate and manage activities, manage the flow of information and resources that manufacture, assemble, and distribute products and materials. Such organizations and people include suppliers, manufacturers, distributors, wholesalers, retailers, and consumers. The supply chains also facilitate relationships between the organizations and people.
At least some known computer-implemented supply chain management systems include sufficient programming to execute supply chain operation based on static data. For example, such systems can determine a least cost method of transporting a product between two points based upon statically-established fuel costs, labor costs, and shipping periods. However, such systems have limited resources for processing the large amounts of data, both static and dynamic data, as well as time sensitive data, typically associated with large-scale, international supply chain networks. Such data includes aging weather forecasts and traffic conditions. Also, such systems lack mechanisms to distinguish between valid data and invalid data.
Many such known systems require routine human intervention to input emergent data and to correct for unforeseen events. For example, human dispatchers facilitate enhancing the efficiency of a fleet by taking into account static variables such as delivery windows, contracted levels of service, and estimated times of arrival (ETA). In addition to these static variables, dispatchers also respond to dynamic conditions, for example, seasonal traffic conditions, unanticipated disruptive weather, and real-time driver behavior. For emergent adverse conditions, the dispatchers react to reduce the impact of those adverse conditions on operation after some period of latency associated with data collection, report delivery, and human deliberation. Furthermore, such systems require humans to perform pre-delivery planning and post-delivery analysis, wherein the post-delivery analysis is used as a feedback mechanism for subsequent pre-delivery planning. Moreover, since such systems lack adaptive cognitive features, including adaptive learning features, subsequent corrections for real-world, real-time, unanticipated events need to be directed by a human agent.